{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [*] Loading model from pretrained/tsp_100/epoch-99.pt\n",
      "[82, 68, 52, 75, 81, 77, 74, 26, 39, 49, 10, 54, 25, 59, 11, 72, 99, 28, 61, 63, 92, 67, 44, 16, 91, 41, 22, 95, 30, 14, 40, 36, 89, 34, 43, 38, 6, 90, 15, 0, 37, 23, 87, 88, 19, 65, 35, 13, 47, 1, 96, 98, 46, 64, 51, 69, 5, 4, 70, 24, 80, 20, 55, 84, 86, 56, 58, 60, 79, 29, 8, 3, 9, 17, 62, 66, 48, 2, 33, 53, 45, 7, 85, 12, 76, 42, 83, 32, 93, 27, 31, 21, 78, 73, 57, 94, 97, 71, 18, 50]\n"
     ]
    }
   ],
   "source": [
    "from utils import load_model\n",
    "model, _ = load_model('pretrained/tsp_100/')\n",
    "model.eval()  # Put in evaluation mode to not track gradients\n",
    "\n",
    "xy = np.random.rand(100, 2)\n",
    "\n",
    "def make_oracle(model, xy, temperature=1.0):\n",
    "    \n",
    "    num_nodes = len(xy)\n",
    "    \n",
    "    xyt = torch.tensor(xy).float()[None]  # Add batch dimension\n",
    "    \n",
    "    with torch.no_grad():  # Inference only\n",
    "        embeddings, _ = model.embedder(model._init_embed(xyt))\n",
    "\n",
    "        # Compute keys, values for the glimpse and keys for the logits once as they can be reused in every step\n",
    "        fixed = model._precompute(embeddings)\n",
    "    \n",
    "    def oracle(tour):\n",
    "        with torch.no_grad():  # Inference only\n",
    "            # Input tour with 0 based indices\n",
    "            # Output vector with probabilities for locations not in tour\n",
    "            tour = torch.tensor(tour).long()\n",
    "            if len(tour) == 0:\n",
    "                step_context = model.W_placeholder\n",
    "            else:\n",
    "                step_context = torch.cat((embeddings[0, tour[0]], embeddings[0, tour[-1]]), -1)\n",
    "\n",
    "            # Compute query = context node embedding, add batch and step dimensions (both 1)\n",
    "            query = fixed.context_node_projected + model.project_step_context(step_context[None, None, :])\n",
    "\n",
    "            # Create the mask and convert to bool depending on PyTorch version\n",
    "            mask = torch.zeros(num_nodes, dtype=torch.uint8) > 0\n",
    "            mask[tour] = 1\n",
    "            mask = mask[None, None, :]  # Add batch and step dimension\n",
    "\n",
    "            log_p, _ = model._one_to_many_logits(query, fixed.glimpse_key, fixed.glimpse_val, fixed.logit_key, mask)\n",
    "            p = torch.softmax(log_p / temperature, -1)[0, 0]\n",
    "            assert (p[tour] == 0).all()\n",
    "            assert (p.sum() - 1).abs() < 1e-5\n",
    "            #assert np.allclose(p.sum().item(), 1)\n",
    "        return p.numpy()\n",
    "    \n",
    "    return oracle\n",
    "        \n",
    "\n",
    "\n",
    "oracle = make_oracle(model, xy)\n",
    "\n",
    "sample = False\n",
    "tour = []\n",
    "tour_p = []\n",
    "while(len(tour) < len(xy)):\n",
    "    p = oracle(tour)\n",
    "    \n",
    "    if sample:\n",
    "        # Advertising the Gumbel-Max trick\n",
    "        g = -np.log(-np.log(np.random.rand(*p.shape)))\n",
    "        i = np.argmax(np.log(p) + g)\n",
    "        # i = np.random.multinomial(1, p)\n",
    "    else:\n",
    "        # Greedy\n",
    "        i = np.argmax(p)\n",
    "    tour.append(i)\n",
    "    tour_p.append(p)\n",
    "    \n",
    "print(tour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from matplotlib.collections import PatchCollection\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "# Code inspired by Google OR Tools plot:\n",
    "# https://github.com/google/or-tools/blob/fb12c5ded7423d524fc6c95656a9bdc290a81d4d/examples/python/cvrptw_plot.py\n",
    "\n",
    "def plot_tsp(xy, tour, ax1):\n",
    "    \"\"\"\n",
    "    Plot the TSP tour on matplotlib axis ax1.\n",
    "    \"\"\"\n",
    "    \n",
    "    ax1.set_xlim(0, 1)\n",
    "    ax1.set_ylim(0, 1)\n",
    "    \n",
    "    xs, ys = xy[tour].transpose()\n",
    "    xs, ys = xy[tour].transpose()\n",
    "    dx = np.roll(xs, -1) - xs\n",
    "    dy = np.roll(ys, -1) - ys\n",
    "    d = np.sqrt(dx * dx + dy * dy)\n",
    "    lengths = d.cumsum()\n",
    "    \n",
    "    # Scatter nodes\n",
    "    ax1.scatter(xs, ys, s=40, color='blue')\n",
    "    # Starting node\n",
    "    ax1.scatter([xs[0]], [ys[0]], s=100, color='red')\n",
    "    \n",
    "    # Arcs\n",
    "    qv = ax1.quiver(\n",
    "        xs, ys, dx, dy,\n",
    "        scale_units='xy',\n",
    "        angles='xy',\n",
    "        scale=1,\n",
    "    )\n",
    "    \n",
    "    ax1.set_title('{} nodes, total length {:.2f}'.format(len(tour), lengths[-1]))\n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "plot_tsp(xy, tour, ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAPRkG1kYXQAAAKtBgX//6ncRem9\n",
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       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.collections import PatchCollection\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib.lines import Line2D\n",
    "from IPython.display import HTML\n",
    "\n",
    "from celluloid import Camera  # pip install celluloid\n",
    "\n",
    "def format_prob(prob):\n",
    "    return ('{:.6f}' if prob > 1e-5 else '{:.2E}').format(prob)\n",
    "\n",
    "def plot_tsp_ani(xy, tour, tour_p=None, max_steps=1000):\n",
    "    n = len(tour)\n",
    "    fig, ax1 = plt.subplots(figsize=(10, 10))\n",
    "    xs, ys = xy[tour].transpose()\n",
    "    dx = np.roll(xs, -1) - xs\n",
    "    dy = np.roll(ys, -1) - ys\n",
    "    d = np.sqrt(dx * dx + dy * dy)\n",
    "    lengths = d.cumsum()\n",
    "    \n",
    "    ax1.set_xlim(0, 1)\n",
    "    ax1.set_ylim(0, 1)\n",
    "\n",
    "    camera = Camera(fig)\n",
    "\n",
    "    total_length = 0\n",
    "    cum_log_prob = 0\n",
    "    for i in range(n + 1):\n",
    "        for plot_probs in [False] if tour_p is None or i >= n else [False, True]:\n",
    "            # Title\n",
    "            title = 'Nodes: {:3d}, length: {:.4f}, prob: {}'.format(\n",
    "                i, lengths[i - 2] if i > 1 else 0., format_prob(np.exp(cum_log_prob))\n",
    "            )\n",
    "            ax1.text(0.6, 0.97, title, transform=ax.transAxes)\n",
    "\n",
    "            # First print current node and next candidates\n",
    "            ax1.scatter(xs, ys, s=40, color='blue')\n",
    "\n",
    "            if i > 0:\n",
    "                ax1.scatter([xs[i - 1]], [ys[i - 1]], s=100, color='red')\n",
    "            if i > 1:\n",
    "                qv = ax1.quiver(\n",
    "                    xs[:i-1],\n",
    "                    ys[:i-1],\n",
    "                    dx[:i-1],\n",
    "                    dy[:i-1],\n",
    "                    scale_units='xy',\n",
    "                    angles='xy',\n",
    "                    scale=1,\n",
    "                )\n",
    "            if plot_probs:\n",
    "                prob_rects = [Rectangle((x, y), 0.01, 0.1 * p) for (x, y), p in zip(xy, tour_p[i]) if p > 0.01]\n",
    "                pc = PatchCollection(prob_rects, facecolor='lightgray', alpha=1.0, edgecolor='lightgray')\n",
    "                ax1.add_collection(pc)\n",
    "            camera.snap()\n",
    "        if i < n and tour_p is not None:\n",
    "            # Add cumulative_probability\n",
    "            cum_log_prob += np.log(tour_p[i][tour[i]])\n",
    "        if i > max_steps:\n",
    "            break\n",
    "\n",
    "    # Plot final tour\n",
    "    # Scatter nodes\n",
    "    ax1.scatter(xs, ys, s=40, color='blue')\n",
    "    # Starting node\n",
    "    ax1.scatter([xs[0]], [ys[0]], s=100, color='red')\n",
    "    \n",
    "    # Arcs\n",
    "    qv = ax1.quiver(\n",
    "        xs, ys, dx, dy,\n",
    "        scale_units='xy',\n",
    "        angles='xy',\n",
    "        scale=1,\n",
    "    )\n",
    "    if tour_p is not None:\n",
    "        # Note this does not use stable logsumexp trick\n",
    "        cum_log_prob = format_prob(np.exp(sum([np.log(p[node]) for node, p in zip(tour, tour_p)])))\n",
    "    else:\n",
    "        cum_log_prob = '?'\n",
    "    ax1.set_title('{} nodes, total length {:.4f}, prob: {}'.format(len(tour), lengths[-1], cum_log_prob))\n",
    "    \n",
    "    camera.snap()\n",
    "    \n",
    "    return camera \n",
    "\n",
    "    \n",
    "animation = plot_tsp_ani(xy, tour, tour_p).animate(interval=500)\n",
    "# animation.save('images/tsp.gif', writer='imagemagick', fps=2)  # requires imagemagick \n",
    "# compress by running 'convert tsp.gif -strip -coalesce -layers Optimize tsp.gif'\n",
    "HTML(animation.to_html5_video())  # requires ffmpeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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